Bayesian hyperparameter optimization, is it worth it?
Efficiently integrating HPO frameworks into an existing project is non-trivial. Most common datasets/tasks already have established architectures/hyperparameters/etc. and require only a few additional tuning parameters. In this case, the benefits (assuming they exist) brought by Bayesian HPO techniques lack behind development time (simplicity), and this is one dominant reason that users prefer grid search or random search over some more sophisticated Bayesian optimization techniques.
For a very large scale problem and an entirely new task (or dataset) for which the user has no intuition on &quot;good hyperparameters&quot;, Bayesian HPO may be a good option to consider over random or grid search. This is because there may be a very large number of hyperparameters (due to insufficient domain knowledge), and integrating HPO into the project may result in far less time than grid search over all possible hyperparameters.